{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "02_pytorch_classification_exercise_solutions.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMN00jzQMAwFrBAxxyYY+e3",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mrdbourke/pytorch-deep-learning/blob/main/extras/solutions/02_pytorch_classification_exercise_solutions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 02. PyTorch Classification Exercise Solutions\n",
        "\n",
        "The following is one possible set (there may be more than one way to do things) of solutions for the 02. PyTorch WorkFlow Exercise template.\n",
        "\n",
        "You can see a live [walkthrough of the solutions (errors and all) on YouTube](https://youtu.be/ByyHwoEgF0Q).\n",
        "\n",
        "See [other solutions on the course GitHub](https://github.com/mrdbourke/pytorch-deep-learning/tree/main/extras/solutions)."
      ],
      "metadata": {
        "id": "ZKJFt7YxH8yl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for GPU\n",
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bUDhp5i0IHps",
        "outputId": "576d649c-6289-4c00-f7b5-a05783f0daeb"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu Feb 10 00:20:37 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   33C    P0    26W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import torch\n",
        "import torch\n",
        "\n",
        "# Setup device agnostic code\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "CSrUPgapO0tf",
        "outputId": "eafe3b29-44fd-4cf2-fdac-6fee7ba1fbb5"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'cuda'"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Make a binary classification dataset with Scikit-Learn's [`make_moons()`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html) function.\n",
        "  * For consistency, the dataset should have 1000 samples and a `random_state=42`.\n",
        "  * Turn the data into PyTorch tensors. \n",
        "  * Split the data into training and test sets using `train_test_split` with 80% training and 20% testing."
      ],
      "metadata": {
        "id": "pH7jIZ2SPFee"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import make_moons\n",
        "\n",
        "NUM_SAMPLES = 1000\n",
        "RANDOM_SEED = 42\n",
        "\n",
        "X, y = make_moons(n_samples=NUM_SAMPLES,\n",
        "                  noise=0.07,\n",
        "                  random_state=RANDOM_SEED)\n",
        "\n",
        "X[:10], y[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5t4VhPV1PX1X",
        "outputId": "ce54b01f-13d7-4575-d328-451d2b16eed8"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[-0.03341062,  0.4213911 ],\n",
              "        [ 0.99882703, -0.4428903 ],\n",
              "        [ 0.88959204, -0.32784256],\n",
              "        [ 0.34195829, -0.41768975],\n",
              "        [-0.83853099,  0.53237483],\n",
              "        [ 0.59906425, -0.28977331],\n",
              "        [ 0.29009023, -0.2046885 ],\n",
              "        [-0.03826868,  0.45942924],\n",
              "        [ 1.61377123, -0.2939697 ],\n",
              "        [ 0.693337  ,  0.82781911]]), array([1, 1, 1, 1, 0, 1, 1, 1, 1, 0]))"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn data into a DataFrame\n",
        "import pandas as pd\n",
        "data_df = pd.DataFrame({\"X0\": X[:, 0],\n",
        "                        \"X1\": X[:, 1],\n",
        "                        \"y\": y})\n",
        "data_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "SUeHZ3-3P9C7",
        "outputId": "5a1883bb-5f38-4fa8-adf8-b217bd11b8f2"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e8bd1659-4e93-41c2-b317-42662b7f838d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X0</th>\n",
              "      <th>X1</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.033411</td>\n",
              "      <td>0.421391</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.998827</td>\n",
              "      <td>-0.442890</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.889592</td>\n",
              "      <td>-0.327843</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.341958</td>\n",
              "      <td>-0.417690</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.838531</td>\n",
              "      <td>0.532375</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e8bd1659-4e93-41c2-b317-42662b7f838d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e8bd1659-4e93-41c2-b317-42662b7f838d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e8bd1659-4e93-41c2-b317-42662b7f838d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         X0        X1  y\n",
              "0 -0.033411  0.421391  1\n",
              "1  0.998827 -0.442890  1\n",
              "2  0.889592 -0.327843  1\n",
              "3  0.341958 -0.417690  1\n",
              "4 -0.838531  0.532375  0"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the data on a plot\n",
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.RdYlBu);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "owrkPSFvQPFI",
        "outputId": "5ce30782-f4bb-476e-e518-4c4594ead6ea"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn data into tensors\n",
        "X = torch.tensor(X, dtype=torch.float)\n",
        "y = torch.tensor(y, dtype=torch.float)\n",
        "\n",
        "# Split the data into train and test sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, \n",
        "                                                    y, \n",
        "                                                    test_size=0.2, \n",
        "                                                    random_state=RANDOM_SEED)\n",
        "\n",
        "len(X_train), len(X_test), len(y_train), len(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bDhyHn9fR4dq",
        "outputId": "0ecff495-9582-439a-b59a-5cde8f8420b4"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(800, 200, 800, 200)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Build a model by subclassing `nn.Module` that incorporates non-linear activation functions and is capable of fitting the data you created in 1.\n",
        "  * Feel free to use any combination of PyTorch layers (linear and non-linear) you want."
      ],
      "metadata": {
        "id": "cMIjxZdzQfPz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "class MoonModelV0(nn.Module):\n",
        "    def __init__(self, in_features, out_features, hidden_units):\n",
        "        super().__init__()\n",
        "        \n",
        "        self.layer1 = nn.Linear(in_features=in_features, \n",
        "                                 out_features=hidden_units)\n",
        "        self.layer2 = nn.Linear(in_features=hidden_units, \n",
        "                                 out_features=hidden_units)\n",
        "        self.layer3 = nn.Linear(in_features=hidden_units,\n",
        "                                out_features=out_features)\n",
        "        self.relu = nn.ReLU()\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.layer3(self.relu(self.layer2(self.relu(self.layer1(x)))))\n",
        "\n",
        "model_0 = MoonModelV0(in_features=2,\n",
        "                      out_features=1,\n",
        "                      hidden_units=10).to(device)\n",
        "model_0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hwtyvm34Ri6Q",
        "outputId": "3cc15c86-e5f7-44e7-cd77-8e82e1046873"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MoonModelV0(\n",
              "  (layer1): Linear(in_features=2, out_features=10, bias=True)\n",
              "  (layer2): Linear(in_features=10, out_features=10, bias=True)\n",
              "  (layer3): Linear(in_features=10, out_features=1, bias=True)\n",
              "  (relu): ReLU()\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_0.state_dict()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cTH8574mWgRI",
        "outputId": "8d205c96-52c4-468d-c2f5-58e8ab5a75be"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('layer1.weight', tensor([[ 0.5406,  0.5869],\n",
              "                      [-0.1657,  0.6496],\n",
              "                      [-0.1549,  0.1427],\n",
              "                      [-0.3443,  0.4153],\n",
              "                      [ 0.6233, -0.5188],\n",
              "                      [ 0.6146,  0.1323],\n",
              "                      [ 0.5224,  0.0958],\n",
              "                      [ 0.3410, -0.0998],\n",
              "                      [ 0.5451,  0.1045],\n",
              "                      [-0.3301,  0.1802]], device='cuda:0')),\n",
              "             ('layer1.bias',\n",
              "              tensor([-0.3258, -0.0829, -0.2872,  0.4691, -0.5582, -0.3260, -0.1997, -0.4252,\n",
              "                       0.0667, -0.6984], device='cuda:0')),\n",
              "             ('layer2.weight',\n",
              "              tensor([[ 0.2856, -0.2686,  0.2441,  0.0526, -0.1027,  0.1954,  0.0493,  0.2555,\n",
              "                        0.0346, -0.0997],\n",
              "                      [ 0.0850, -0.0858,  0.1331,  0.2823,  0.1828, -0.1382,  0.1825,  0.0566,\n",
              "                        0.1606, -0.1927],\n",
              "                      [-0.3130, -0.1222, -0.2426,  0.2595,  0.0911,  0.1310,  0.1000, -0.0055,\n",
              "                        0.2475, -0.2247],\n",
              "                      [ 0.0199, -0.2158,  0.0975, -0.1089,  0.0969, -0.0659,  0.2623, -0.1874,\n",
              "                       -0.1886, -0.1886],\n",
              "                      [ 0.2844,  0.1054,  0.3043, -0.2610, -0.3137, -0.2474, -0.2127,  0.1281,\n",
              "                        0.1132,  0.2628],\n",
              "                      [-0.1633, -0.2156,  0.1678, -0.1278,  0.1919, -0.0750,  0.1809, -0.2457,\n",
              "                       -0.1596,  0.0964],\n",
              "                      [ 0.0669, -0.0806,  0.1885,  0.2150, -0.2293, -0.1688,  0.2896, -0.1067,\n",
              "                       -0.1121, -0.3060],\n",
              "                      [-0.1811,  0.0790, -0.0417, -0.2295,  0.0074, -0.2160, -0.2683, -0.1741,\n",
              "                       -0.2768, -0.2014],\n",
              "                      [ 0.3161,  0.0597,  0.0974, -0.2949, -0.2077, -0.1053,  0.0494, -0.2783,\n",
              "                       -0.1363, -0.1893],\n",
              "                      [ 0.0009, -0.1177, -0.0219, -0.2143, -0.2171, -0.1845, -0.1082, -0.2496,\n",
              "                        0.2651, -0.0628]], device='cuda:0')),\n",
              "             ('layer2.bias',\n",
              "              tensor([ 0.2721,  0.0985, -0.2678,  0.2188, -0.0870, -0.1212, -0.2625, -0.3144,\n",
              "                       0.0905, -0.0691], device='cuda:0')),\n",
              "             ('layer3.weight',\n",
              "              tensor([[ 0.1231, -0.2595,  0.2348, -0.2321, -0.0546,  0.0661,  0.1633,  0.2553,\n",
              "                        0.2881, -0.2507]], device='cuda:0')),\n",
              "             ('layer3.bias', tensor([0.0796], device='cuda:0'))])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Setup a binary classification compatible loss function and optimizer to use when training the model built in 2."
      ],
      "metadata": {
        "id": "DSj97RwyVeFE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss_fn = nn.BCEWithLogitsLoss() # sigmoid layer built-in\n",
        "# loss_fn = nn.BCELoss() # requires sigmoid layer\n",
        "optimizer = torch.optim.SGD(params=model_0.parameters(), # parameters of model to optimize \n",
        "                            lr=0.1) # learning rate"
      ],
      "metadata": {
        "id": "whSGw5qgVvxU"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Create a training and testing loop to fit the model you created in 2 to the data you created in 1.\n",
        "  * To measure model accuray, you can create your own accuracy function or use the accuracy function in [TorchMetrics](https://torchmetrics.readthedocs.io/en/latest/).\n",
        "  * Train the model for long enough for it to reach over 96% accuracy.\n",
        "  * The training loop should output progress every 10 epochs of the model's training and test set loss and accuracy."
      ],
      "metadata": {
        "id": "nvk4PfNTWUAt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# What's coming out of our model?\n",
        "\n",
        "# logits (raw outputs of model)\n",
        "print(\"Logits:\")\n",
        "print(model_0(X_train.to(device)[:10]).squeeze())\n",
        "\n",
        "# Prediction probabilities\n",
        "print(\"Pred probs:\")\n",
        "print(torch.sigmoid(model_0(X_train.to(device)[:10]).squeeze()))\n",
        "\n",
        "# Prediction probabilities\n",
        "print(\"Pred labels:\")\n",
        "print(torch.round(torch.sigmoid(model_0(X_train.to(device)[:10]).squeeze())))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AgnFdlamd2-D",
        "outputId": "3c0b0c7e-327d-4d59-9b42-1bf39c8d5ced"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logits:\n",
            "tensor([0.0019, 0.0094, 0.0161, 0.0185, 0.0284, 0.0192, 0.0291, 0.0196, 0.0258,\n",
            "        0.0079], device='cuda:0', grad_fn=<SqueezeBackward0>)\n",
            "Pred probs:\n",
            "tensor([0.5005, 0.5024, 0.5040, 0.5046, 0.5071, 0.5048, 0.5073, 0.5049, 0.5065,\n",
            "        0.5020], device='cuda:0', grad_fn=<SigmoidBackward0>)\n",
            "Pred labels:\n",
            "tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.], device='cuda:0',\n",
            "       grad_fn=<RoundBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's calculate the accuracy\n",
        "!pip -q install torchmetrics # colab doesn't come with torchmetrics\n",
        "from torchmetrics import Accuracy\n",
        "acc_fn = Accuracy(task=\"multiclass\", num_classes=2).to(device) # send accuracy function to device\n",
        "acc_fn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rUSDNHB4euoJ",
        "outputId": "b16bb6fe-4be2-49af-94ce-9c90877635b1"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MulticlassAccuracy()"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(RANDOM_SEED)\n",
        "\n",
        "epochs=1000\n",
        "\n",
        "# Send data to the device\n",
        "X_train, y_train = X_train.to(device), y_train.to(device)\n",
        "X_test, y_test = X_test.to(device), y_test.to(device)\n",
        "\n",
        "# Loop through the data\n",
        "for epoch in range(epochs):\n",
        "  ### Training\n",
        "  model_0.train()\n",
        "\n",
        "  # 1. Forward pass\n",
        "  y_logits = model_0(X_train).squeeze()\n",
        "  # print(y_logits[:5]) # model raw outputs are \"logits\"\n",
        "  y_pred_probs = torch.sigmoid(y_logits)\n",
        "  y_pred = torch.round(y_pred_probs)\n",
        "\n",
        "  # 2. Calculaute the loss\n",
        "  loss = loss_fn(y_logits, y_train) # loss = compare model raw outputs to desired model outputs\n",
        "  acc = acc_fn(y_pred, y_train.int()) # the accuracy function needs to compare pred labels (not logits) with actual labels\n",
        "\n",
        "  # 3. Zero the gradients\n",
        "  optimizer.zero_grad()\n",
        "\n",
        "  # 4. Loss backward (perform backpropagation) - https://brilliant.org/wiki/backpropagation/#:~:text=Backpropagation%2C%20short%20for%20%22backward%20propagation,to%20the%20neural%20network's%20weights.\n",
        "  loss.backward()\n",
        "\n",
        "  # 5. Step the optimizer (gradient descent) - https://towardsdatascience.com/gradient-descent-algorithm-a-deep-dive-cf04e8115f21#:~:text=Gradient%20descent%20(GD)%20is%20an,e.g.%20in%20a%20linear%20regression) \n",
        "  optimizer.step()\n",
        "\n",
        "  ### Testing\n",
        "  model_0.eval() \n",
        "  with torch.inference_mode():\n",
        "    # 1. Forward pass\n",
        "    test_logits = model_0(X_test).squeeze()\n",
        "    test_pred = torch.round(torch.sigmoid(test_logits))\n",
        "    # 2. Caculate the loss/acc\n",
        "    test_loss = loss_fn(test_logits, y_test)\n",
        "    test_acc = acc_fn(test_pred, y_test.int()) \n",
        "\n",
        "  # Print out what's happening\n",
        "  if epoch % 100 == 0:\n",
        "    print(f\"Epoch: {epoch} | Loss: {loss:.2f} Acc: {acc:.2f} | Test loss: {test_loss:.2f} Test acc: {test_acc:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SHBY3h7XXnxt",
        "outputId": "dccc49c8-eba7-483a-b59b-6af4614a8abc"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 | Loss: 0.70 Acc: 0.37 | Test loss: 0.69 Test acc: 0.50\n",
            "Epoch: 100 | Loss: 0.39 Acc: 0.82 | Test loss: 0.40 Test acc: 0.76\n",
            "Epoch: 200 | Loss: 0.24 Acc: 0.88 | Test loss: 0.24 Test acc: 0.89\n",
            "Epoch: 300 | Loss: 0.20 Acc: 0.91 | Test loss: 0.19 Test acc: 0.94\n",
            "Epoch: 400 | Loss: 0.17 Acc: 0.93 | Test loss: 0.15 Test acc: 0.94\n",
            "Epoch: 500 | Loss: 0.12 Acc: 0.95 | Test loss: 0.11 Test acc: 0.96\n",
            "Epoch: 600 | Loss: 0.08 Acc: 0.98 | Test loss: 0.07 Test acc: 0.99\n",
            "Epoch: 700 | Loss: 0.06 Acc: 0.99 | Test loss: 0.05 Test acc: 1.00\n",
            "Epoch: 800 | Loss: 0.04 Acc: 0.99 | Test loss: 0.03 Test acc: 1.00\n",
            "Epoch: 900 | Loss: 0.03 Acc: 1.00 | Test loss: 0.02 Test acc: 1.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Make predictions with your trained model and plot them using the `plot_decision_boundary()` function created in this notebook."
      ],
      "metadata": {
        "id": "8Nwihtomj9JO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the model predictions\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "# TK - this could go in the helper_functions.py and be explained there\n",
        "def plot_decision_boundary(model, X, y):\n",
        "  \n",
        "    # Put everything to CPU (works better with NumPy + Matplotlib)\n",
        "    model.to(\"cpu\")\n",
        "    X, y = X.to(\"cpu\"), y.to(\"cpu\")\n",
        "\n",
        "    # Source - https://madewithml.com/courses/foundations/neural-networks/ \n",
        "    # (with modifications)\n",
        "    x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n",
        "    y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n",
        "    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 101), \n",
        "                         np.linspace(y_min, y_max, 101))\n",
        "\n",
        "    # Make features\n",
        "    X_to_pred_on = torch.from_numpy(np.column_stack((xx.ravel(), yy.ravel()))).float()\n",
        "\n",
        "    # Make predictions\n",
        "    model.eval()\n",
        "    with torch.inference_mode():\n",
        "        y_logits = model(X_to_pred_on)\n",
        "\n",
        "    # Test for multi-class or binary and adjust logits to prediction labels\n",
        "    if len(torch.unique(y)) > 2:\n",
        "        y_pred = torch.softmax(y_logits, dim=1).argmax(dim=1) # mutli-class\n",
        "    else: \n",
        "        y_pred = torch.round(torch.sigmoid(y_logits)) # binary\n",
        "    \n",
        "    # Reshape preds and plot\n",
        "    y_pred = y_pred.reshape(xx.shape).detach().numpy()\n",
        "    plt.contourf(xx, yy, y_pred, cmap=plt.cm.RdYlBu, alpha=0.7)\n",
        "    plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n",
        "    plt.xlim(xx.min(), xx.max())\n",
        "    plt.ylim(yy.min(), yy.max())"
      ],
      "metadata": {
        "id": "0YRzatb8a1P2"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot decision boundaries for training and test sets\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_decision_boundary(model_0, X_train, y_train)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_decision_boundary(model_0, X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "PMrcpyirig1d",
        "outputId": "efc8e43a-5174-4a7d-850b-d040b8d0ad47"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6. Replicate the Tanh (hyperbolic tangent) activation function in pure PyTorch.\n",
        "  * Feel free to reference the [ML cheatsheet website](https://ml-cheatsheet.readthedocs.io/en/latest/activation_functions.html#tanh) for the formula."
      ],
      "metadata": {
        "id": "EtMYBvtciiAU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tensor_A = torch.arange(-100, 100, 1)\n",
        "plt.plot(tensor_A)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "BlXaWC5TkEUE",
        "outputId": "c6ce6e54-f018-40b9-f4a9-167609aab00d"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f37332d5c10>]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(torch.tanh(tensor_A))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "vZPCcQmIkZjO",
        "outputId": "8eba85ad-4577-4cd0-9e35-3524ac059720"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f3733254b10>]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def tanh(x):\n",
        "  # Source - https://ml-cheatsheet.readthedocs.io/en/latest/activation_functions.html#tanh\n",
        "  return (torch.exp(x) - torch.exp(-x)) / (torch.exp(x) + torch.exp(-x))\n",
        "\n",
        "plt.plot(tanh(tensor_A))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "J-ne__Kjkdc1",
        "outputId": "57679b0d-a17c-435a-8091-7de340ed4c79"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f37331d55d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7. Create a multi-class dataset using the [spirals data creation function from CS231n](https://cs231n.github.io/neural-networks-case-study/) (see below for the code).\n",
        "  * Split the data into training and test sets (80% train, 20% test) as well as turn it into PyTorch tensors.\n",
        "  * Construct a model capable of fitting the data (you may need a combination of linear and non-linear layers).\n",
        "  * Build a loss function and optimizer capable of handling multi-class data (optional extension: use the Adam optimizer instead of SGD, you may have to experiment with different values of the learning rate to get it working).\n",
        "  * Make a training and testing loop for the multi-class data and train a model on it to reach over 95% testing accuracy (you can use any accuracy measuring function here that you like).\n",
        "  * Plot the decision boundaries on the spirals dataset from your model predictions, the `plot_decision_boundary()` function should work for this dataset too."
      ],
      "metadata": {
        "id": "Lbt1bNcWk5G9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Code for creating a spiral dataset from CS231n\n",
        "import numpy as np\n",
        "RANDOM_SEED = 42\n",
        "np.random.seed(RANDOM_SEED)\n",
        "N = 100 # number of points per class\n",
        "D = 2 # dimensionality\n",
        "K = 3 # number of classes\n",
        "X = np.zeros((N*K,D)) # data matrix (each row = single example)\n",
        "y = np.zeros(N*K, dtype='uint8') # class labels\n",
        "for j in range(K):\n",
        "  ix = range(N*j,N*(j+1))\n",
        "  r = np.linspace(0.0,1,N) # radius\n",
        "  t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta\n",
        "  X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]\n",
        "  y[ix] = j\n",
        "# lets visualize the data\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "tU-UNZsKlJls",
        "outputId": "f9e160bd-6546-4b70-9dee-15a684c924d0"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn data into tensors\n",
        "X = torch.from_numpy(X).type(torch.float) # features as float32\n",
        "y = torch.from_numpy(y).type(torch.LongTensor) # labels need to be of type long\n",
        "\n",
        "# Create train and test splits\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=RANDOM_SEED)\n",
        "len(X_train), len(X_test), len(y_train), len(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OWVrmkEyl0VP",
        "outputId": "9fb43744-2c44-413e-cf7a-db6e9d22f4e6"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(240, 60, 240, 60)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's calculate the accuracy for when we fit our model\n",
        "!pip -q install torchmetrics # colab doesn't come with torchmetrics\n",
        "from torchmetrics import Accuracy\n",
        "acc_fn = Accuracy(task=\"multiclass\", num_classes=3).to(device) # send accuracy function to device\n",
        "acc_fn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a-v-7f0op0tG",
        "outputId": "29f8c954-2bbe-4f80-fcd7-c578c576c925"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MulticlassAccuracy()"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare device agnostic code\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "class SpiralModel(nn.Module): \n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.linear1 = nn.Linear(in_features=2, out_features=10)\n",
        "    self.linear2 = nn.Linear(in_features=10, out_features=10)\n",
        "    self.linear3 = nn.Linear(in_features=10, out_features=3)\n",
        "    self.relu = nn.ReLU()\n",
        "\n",
        "  def forward(self, x):\n",
        "    return self.linear3(self.relu(self.linear2(self.relu(self.linear1(x)))))\n",
        "\n",
        "model_1 = SpiralModel().to(device)\n",
        "model_1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DB3u3ldumapf",
        "outputId": "d876f3bd-9eb9-45b0-ffbc-f47250da1616"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SpiralModel(\n",
              "  (linear1): Linear(in_features=2, out_features=10, bias=True)\n",
              "  (linear2): Linear(in_features=10, out_features=10, bias=True)\n",
              "  (linear3): Linear(in_features=10, out_features=3, bias=True)\n",
              "  (relu): ReLU()\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup data to be device agnostic\n",
        "X_train, y_train = X_train.to(device), y_train.to(device)\n",
        "X_test, y_test = X_test.to(device), y_test.to(device)\n",
        "print(X_train.dtype, X_test.dtype, y_train.dtype, y_test.dtype)\n",
        "\n",
        "# Print out untrained model outputs\n",
        "print(\"Logits:\")\n",
        "print(model_1(X_train)[:10])\n",
        "\n",
        "print(\"Pred probs:\")\n",
        "print(torch.softmax(model_1(X_train)[:10], dim=1))\n",
        "\n",
        "print(\"Pred labels:\")\n",
        "print(torch.softmax(model_1(X_train)[:10], dim=1).argmax(dim=1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QE7XWSSunMTS",
        "outputId": "487527b2-8364-4d4f-a5a3-f97ed583473a"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.float32 torch.float32 torch.int64 torch.int64\n",
            "Logits:\n",
            "tensor([[-0.2160, -0.0600,  0.2256],\n",
            "        [-0.2020, -0.0530,  0.2257],\n",
            "        [-0.2223, -0.0604,  0.2384],\n",
            "        [-0.2174, -0.0555,  0.2826],\n",
            "        [-0.2201, -0.0502,  0.2792],\n",
            "        [-0.2195, -0.0565,  0.2457],\n",
            "        [-0.2212, -0.0581,  0.2440],\n",
            "        [-0.2251, -0.0631,  0.2354],\n",
            "        [-0.2116, -0.0548,  0.2336],\n",
            "        [-0.2170, -0.0552,  0.2842]], device='cuda:0',\n",
            "       grad_fn=<SliceBackward0>)\n",
            "Pred probs:\n",
            "tensor([[0.2685, 0.3139, 0.4176],\n",
            "        [0.2707, 0.3142, 0.4151],\n",
            "        [0.2659, 0.3126, 0.4215],\n",
            "        [0.2615, 0.3074, 0.4311],\n",
            "        [0.2609, 0.3092, 0.4299],\n",
            "        [0.2653, 0.3123, 0.4224],\n",
            "        [0.2653, 0.3123, 0.4224],\n",
            "        [0.2659, 0.3127, 0.4214],\n",
            "        [0.2681, 0.3136, 0.4184],\n",
            "        [0.2614, 0.3072, 0.4314]], device='cuda:0', grad_fn=<SoftmaxBackward0>)\n",
            "Pred labels:\n",
            "tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2], device='cuda:0')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup loss function and optimizer\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model_1.parameters(),\n",
        "                             lr=0.02)"
      ],
      "metadata": {
        "id": "54EqLRKLo0AW"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Build a training loop for the model\n",
        "epochs = 1000\n",
        "\n",
        "# Loop over data\n",
        "for epoch in range(epochs):\n",
        "  ## Training\n",
        "  model_1.train()\n",
        "  # 1. forward pass\n",
        "  y_logits = model_1(X_train)\n",
        "  y_pred = torch.softmax(y_logits, dim=1).argmax(dim=1)\n",
        "\n",
        "  # 2. calculate the loss\n",
        "  loss = loss_fn(y_logits, y_train)\n",
        "  acc = acc_fn(y_pred, y_train)\n",
        "  \n",
        "  # 3. optimizer zero grad\n",
        "  optimizer.zero_grad()\n",
        "\n",
        "  # 4. loss backwards\n",
        "  loss.backward()\n",
        "\n",
        "  # 5. optimizer step step step\n",
        "  optimizer.step()\n",
        "\n",
        "  ## Testing\n",
        "  model_1.eval()\n",
        "  with torch.inference_mode():\n",
        "    # 1. Forward pass\n",
        "    test_logits = model_1(X_test)\n",
        "    test_pred = torch.softmax(test_logits, dim=1).argmax(dim=1)\n",
        "    # 2. Caculate loss and acc\n",
        "    test_loss = loss_fn(test_logits, y_test)\n",
        "    test_acc = acc_fn(test_pred, y_test)\n",
        "\n",
        "  # Print out what's happening\n",
        "  if epoch % 100 == 0:\n",
        "    print(f\"Epoch: {epoch} | Loss: {loss:.2f} Acc: {acc:.2f} | Test loss: {test_loss:.2f} Test acc: {test_acc:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vIlExkUHnmxi",
        "outputId": "1aecbbe1-cb70-471a-daf2-469fc4a31806"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 | Loss: 1.12 Acc: 0.32 | Test loss: 0.91 Test acc: 0.37\n",
            "Epoch: 100 | Loss: 0.45 Acc: 0.78 | Test loss: 0.32 Test acc: 0.68\n",
            "Epoch: 200 | Loss: 0.12 Acc: 0.96 | Test loss: 0.09 Test acc: 0.98\n",
            "Epoch: 300 | Loss: 0.07 Acc: 0.98 | Test loss: 0.02 Test acc: 1.00\n",
            "Epoch: 400 | Loss: 0.05 Acc: 0.98 | Test loss: 0.01 Test acc: 1.00\n",
            "Epoch: 500 | Loss: 0.04 Acc: 0.99 | Test loss: 0.01 Test acc: 1.00\n",
            "Epoch: 600 | Loss: 0.03 Acc: 0.99 | Test loss: 0.01 Test acc: 1.00\n",
            "Epoch: 700 | Loss: 0.03 Acc: 0.99 | Test loss: 0.00 Test acc: 1.00\n",
            "Epoch: 800 | Loss: 0.02 Acc: 0.99 | Test loss: 0.00 Test acc: 1.00\n",
            "Epoch: 900 | Loss: 0.02 Acc: 0.99 | Test loss: 0.00 Test acc: 1.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot decision boundaries for training and test sets\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_decision_boundary(model_1, X_train, y_train)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_decision_boundary(model_1, X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "JrwVRbaE0keT",
        "outputId": "e5320e0d-748c-4fd8-ee14-3147c862faaf"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}
